food truck parking location assignments - george mason · 2015. 5. 12. · preference totals...

Food Truck Parking Location

Assignments

Siamak Khaledi

Ankit Shah

Matt Shoaf

Sponsor: Karen Wrege

Agenda

• Problem Domain

• Analysis of Existing System

• Proposed Solution

• Prototype

• Validation

• Conclusions

2

Problem Domain

Background• DC, Maryland and Virginia Food

Truck Association (DMVFTA)

• Mobile food vending zones

• Limited parking spaces for food

trucks

• “Lottery-based” assignment has

been used in DC

• Licensed trucks vs Spaces available

(Jan 2015)

• 250 licenses

• 104 parking spaces

3

Current System Overview

• Lottery System

• Receives vendor preferences as input

• Provides location assignment as output

• Black Box

• Online, Web-Based Interface

• Interface design is cumbersome

• Time consuming process

• System does not maintain user preference data

• Under-Utilization and Lack of Vendor Buy-In

• Assignments are static for 1 month, upon receipt by vendors

• Low sign up fee

4

Current System Input/Output

5

Stakeholders• Primary Stakeholders

• DMVFTA – Implement more efficient assignment mechanism

• Vendors – Concerns over current system fairness

• DCRA – Safety, quality of service, utilization level

• Stakeholder Tensions

• Vendors look for more popular locations to park their trucks

• DCRA wants to avoid confrontations and fights on the streets

• DCRA concerned about low utilization

• DMVFTA looking to find the solution to assign spaces fairly to

vendors

• Lottery system fails to utilize available spaces

*DCRA: Department of Consumer and Regulatory Affairs 6

Problem Statement

7

• DCRA is concerned with:

• Under-utilization of assigned spaces.

• Strategic gaming and even abandonment of the lottery system.

• Truck vendors do not perceive the system to be fair.

• DMVFTA wants to develop and prototype alternative primary mechanism to

assign parking spaces to the food truck vendors.

• The central issue: How do we define and measure “fairness” in this problem

domain and develop a system that is superior to the existing one?

Analysis of Existing System

Data Collection

Quantitative:

• Preference/Assignment data

• 8 months of data

• 17 licensed trucks

Qualitative:

• Surveys and discussions with

vendors

• Prefer to avoid distant assignments for

consecutive days

• Not willing to commit to monthly

assignments

• Concerns over current system

fairness/transparency

8

Additional Information

Location space capacities

• Farragut Square 17 spaces

• Franklin Square 17 spaces

• L’Enfant Plaza 18 spaces

• Metro Center 13 spaces

• Navy Yard 8 spaces

• Patriots Plaza 4 spaces

• Union Station 14 spaces

• Virginia Avenue (State Dept) – 10 spaces

• Waterfront Metro 3 spaces

9

Data Analysis – Current Algorithm Behavior

• 8 months of assignment data (what they wanted, what they got)

• Vendors A-Q

• Number of times each vendor got their 1st-3rd preferences

• This is how we measure fairness!

10

Preference Totals

Location 1st Preference 2nd Preference 3rd Preference Total

Farragut Square 17th St 195 177 191 563

Franklin Square 13th St 66 78 125 269

Union Station 91 84 155 330

L'Enfant Plaza 162 133 120 415

Metro Center 228 292 183 703

Waterfront Metro 22 16 16 54

Navy Yard/Capital River Front 38 39 33 110

Patriots Plaza 67 60 60 187

Virginia Ave (State Dept) 75 59 91 225

Hot Locations

(Total Preferences: 1st, 2nd 3rd Choice)

11

Two Variable Analysis of

Location Value

• Number represents

percentage of

requests for a given

spot on each day

12

Farragut Fridays!

• A little history

lesson . . .

Preference Matrix: 2nd Choice

Location Monday Tuesday Wednesday Thursday Friday

Farragut Square 17th St 3.09% 2.24% 3.84% 5.44% 4.26%

Franklin Square 13th St 1.39% 1.17% 1.39% 1.71% 2.67%

Union Station 4.05% 2.35% 0.96% 0.85% 0.75%

L'Enfant Plaza 1.07% 1.71% 3.94% 4.58% 2.88%

Metro Center 4.05% 7.04% 6.72% 5.54% 7.78%

Waterfront Metro 0.75% 0.43% 0.21% 0.32% 0.00%

Navy Yard/Capital River Front 2.13% 1.17% 0.21% 0.53% 0.11%

Patriots Plaza 1.71% 1.81% 1.60% 0.43% 0.85%

Virginia Ave (State Dept) 1.92% 2.24% 1.28% 0.75% 0.11%

Farragut Square on Fridays

• Focus on most popular pick

• Clearly unbalanced results

13

Favorable Results

(8 Month Interval,

Farragut-Friday)

Requirements• Input/Output

• Receive parking location preferences from food truck vendors.

• Output location assignments to vendors.

• Assign parking spaces to vendors based on user preferences.

• Functional

• Provide equal opportunities to vendors to pick their preferences across

all days of the week.

• Utilize a structured query database to store user profile information and

process user requests.

• System Wide

• Maintain historical location preference data.

• Provide web access.

• Include a user interface for vendors.

• Provide secure access.14

Proposed Solution

Proposed Solution

15

• Improved Interface

• Ability to change and maintain preferences

• Weekly assignment schedule

• New Algorithm

• Based on proposed NBA Wheel Draft

• Designed to address both actual and

perceived fairness

Proposed Design

16

Authentication Page

Truck License

Password

User

Truck Info Current Week Next Week

Details of the truck

Home page

Settings

Continue

Login

VSPTrade

Name

License

StatusType VIN Make

Expiry

Date

17

Secondary

Trading

Mechanism

Draft Algorithm

• Wheel draft proposal for NBA [1]

– 30 teams / 30 draft numbers

– 1-6 considered equally valuable

– 5 groups of 6• 1-6

• 25-30

• 19-24

• 13-18

• 7-12

[1] http://www.celticsblog.com/2014/5/20/5735850/the-nba-draft-lottery-wheel-a-proposal-to-solve-the-leagues-draft-zarren-fixed-solution

18

DC Problem Dimension

• Based on the data analysis, the top 3 popular

streets are

– L’Enfant Plaza

– Farragut Square

– Metro Center

• Draft ticket 1-12 are considered equally

valuable

• 1-100 guaranteed a space

• Above 100 gets an off day

• 21 groups of 12 would give every vendor

equal chances on each day of the week

19

Expanded Wheel

1 2 3 4 5 6 7 8 9 10 11 12

145 146 147 148 149 150 151 152 153 154 155 156

97 98 99 100 101 102 103 104 105 106 107 108

25 26 27 28 29 30 31 32 33 34 35 36

181 182 183 184 185 186 187 188 189 190 191 192

37 38 39 40 41 42 43 44 45 46 47 48

133 134 135 136 137 138 139 140 141 142 143 144

193 194 195 196 197 198 199 200 201 202 203 204

49 50 51 52 53 54 55 56 57 58 59 60

217 218 219 220 221 222 223 224 225 226 227 228

229 230 231 232 233 234 235 236 237 238 239 240

13 14 15 16 17 18 19 20 21 22 23 24

109 110 111 112 113 114 115 116 117 118 119 120

241 242 243 244 245 246 247 248 249 250 251 252

85 86 87 88 89 90 91 92 93 94 95 96

157 158 159 160 161 162 163 164 165 166 167 168

73 74 75 76 77 78 79 80 81 82 83 84

121 122 123 124 125 126 127 128 129 130 131 132

169 170 171 172 173 174 175 176 177 178 179 180

61 62 63 64 65 66 67 68 69 70 71 72

205 206 207 208 209 210 211 212 213 214 215 216

• Due to the limited capacity (252 trucks vs 100 locations) it is inevitable to have

several off days

• Insert working days in a way to equally space the days off, avoid consecutive off

days as much as possible

20

Expanded Wheel

1 2 3 4 5 6 7 8 9 10 11 12

145 146 147 148 149 150 151 152 153 154 155 156

97 98 99 100 101 102 103 104 105 106 107 108

25 26 27 28 29 30 31 32 33 34 35 36

181 182 183 184 185 186 187 188 189 190 191 192

37 38 39 40 41 42 43 44 45 46 47 48

133 134 135 136 137 138 139 140 141 142 143 144

193 194 195 196 197 198 199 200 201 202 203 204

49 50 51 52 53 54 55 56 57 58 59 60

217 218 219 220 221 222 223 224 225 226 227 228

229 230 231 232 233 234 235 236 237 238 239 240

13 14 15 16 17 18 19 20 21 22 23 24

109 110 111 112 113 114 115 116 117 118 119 120

241 242 243 244 245 246 247 248 249 250 251 252

85 86 87 88 89 90 91 92 93 94 95 96

157 158 159 160 161 162 163 164 165 166 167 168

73 74 75 76 77 78 79 80 81 82 83 84

121 122 123 124 125 126 127 128 129 130 131 132

169 170 171 172 173 174 175 176 177 178 179 180

61 62 63 64 65 66 67 68 69 70 71 72

205 206 207 208 209 210 211 212 213 214 215 216

• Due to the limited capacity (252 trucks vs 100 locations) it is inevitable to have

several off days

• Insert working days in a way to equally space the off days, avoid consecutive off

days as much as possible

20

Demonstration

Prototype

http://foodtrucksparkingspots.azurewebsites.net/

21

• Visual Studio

Express

• SQL Server

Express

• Microsoft

Azure Cloud

Computing

Validation

Validation

22

Recall: New algorithm needs to be

• Fair in giving all vendors same chances to get their top preferences

• Fair in distribution of days of the week for the top preferences

Validation

22

Recall: New algorithm needs to be

• Fair in giving all vendors same chances to get their top preferences

• Fair in distribution of days of the week for the top preferences

Group Mon Tue Wed Thu Fri

1-12

13-24

25-36

37-48

49-60

61-72

73-84

85-96

97-108

109-120

121-132

133-144

145-156

157-168

169-180

181-192

193-204

205-216

217-228

229-240

241-252

Validation (month 1)

22


1-12 1

13-24 1

25-36 1

37-48 1

49-60 1

61-72 1

73-84 1

85-96 1

97-108 1

109-120 1

121-132 1

133-144 1

145-156 1

157-168 1

169-180 1

181-192 1

193-204 1

205-216 1

217-228 1

229-240 1

241-252 1

mo

nth

1

22



1-12 1 1

13-24 1 1

25-36 1 1

37-48 1 1

49-60 1 1

61-72 1 1

73-84 1 1

85-96 1 1

97-108 1 1

109-120 1 1

121-132 1 1

133-144 1 1

145-156 1 1

157-168 1 1

169-180 1 1

181-192 1 1

193-204 1 1

205-216 1 1

217-228 1 1

229-240 1 1

241-252 1 1

mo

nth

2

22



1-12 1 1 1

13-24 1 1 1

25-36 1 1 1

37-48 1 1 1

49-60 1 1 1

61-72 1 1 1

73-84 1 1 1

85-96 1 1 1

97-108 1 1 1

109-120 1 1 1

121-132 1 1 1

133-144 1 1 1

145-156 1 1 1

157-168 1 1 1

169-180 1 1 1

181-192 1 1 1

193-204 1 1 1

205-216 1 1 1

217-228 1 1 1

229-240 1 1 1

241-252 1 1 1

mo

nth

3

22



1-12 1 1 1 1

13-24 1 1 1 1

25-36 1 1 1 1

37-48 1 1 1 1

49-60 1 1 1 1

61-72 1 1 1 1

73-84 1 1 1 1

85-96 1 1 1 1

97-108 1 1 1 1

109-120 1 1 1 1

121-132 1 1 1 1

133-144 1 1 1 1

145-156 1 1 1 1

157-168 1 1 1 1

169-180 1 1 1 1

181-192 1 1 1 1

193-204 1 1 1 1

205-216 1 1 1 1

217-228 1 1 1 1

229-240 1 1 1 1

241-252 1 1 1 1

mo

nth

4

22



1-12 1 1 1 1 1

13-24 1 1 1 1 1

25-36 1 1 1 1 1

37-48 1 1 1 1 1

49-60 1 1 1 1 1

61-72 1 1 1 1 1

73-84 1 1 1 1 1

85-96 1 1 1 1 1

97-108 1 1 1 1 1

109-120 1 1 1 1 1

121-132 1 1 1 1 1

133-144 1 1 1 1 1

145-156 1 1 1 1 1

157-168 1 1 1 1 1

169-180 1 1 1 1 1

181-192 1 1 1 1 1

193-204 1 1 1 1 1

205-216 1 1 1 1 1

217-228 1 1 1 1 1

229-240 1 1 1 1 1

241-252 1 1 1 1 1

mo

nth

5

Validation (summary)

23

• Proposed algorithm provides

– Equal chances to get preferences

– Equal chances to get all weekdays

• Cycle completion length is 105 days

• A new wheel generation after cycle completion

Conclusions

Fairness Comparison

• Current System

– Recap: Unfair after 8

months

– Data on a sample of 17

trucks

24

Fairness Comparison

• Current System

– Recap: Unfair after 8

months

– Data on a sample of 17

trucks

• Proposed Algorithm

– Simulated: Fair after 5 months

– 17 trucks, assumed to choose most popular locations

24

Conclusions

Proposed system• Mathematically guaranteed to give everyone equitable

assignments after ~ 5 months (105 working days) in worst

case

• May provide equitable assignment sooner, depending on vendor preferences

Comparison• Proposed Algorithm: Fair after 5 months

• Current Algorithm: Unfair after 8 months

25

Recommendations/Future Work

• Integration of primary and secondary

assignment mechanisms

• Machine learning

• Increased capacity (DCRA tension)

26

Acknowledgements

• Project Sponsor

– Karen Wrege (DMVFTA)

• Faculty Advisors

– Dr. Karla Hoffman

– Dr. Andrew Loerch

– Dr. Kathryn Laskey

– Dr. Philip Barry

27

Back-up Slides

Previous Research

• Snake algorithm

– http://www.aaai.org/ocs/index.php/IAAI/IAAI14/paper/download/8341/8657

Data Flow Diagram (detailed)

Object Model

System Architecture

food truck parking location assignments - george mason · 2015. 5. 12. · preference totals...

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